Evidence map›Paper›PMID 38047125›Full record

ArticleEFSA journal. European Food Safety Authority2023

Cumulative risk assessment with pesticides in the framework of MRL setting.

Ingo Grosssteiner, Alexandra Mienne, Laurine Lucas, Pierre L-Yvonnet, Charlotte Trenteseaux, Kevin Fontaine, Xavier Sarda

Abstract read
In one paragraph

Article in EFSA journal. European Food Safety Authority, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

  1. Review
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  8. Cumulative risk assessment with pesticides in the framework of MRL setting.EFSA journal. European Food Safety Authority · 2023
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Ingo Grosssteiner
Alexandra Mienne
Laurine Lucas
Pierre L-Yvonnet
Charlotte Trenteseaux
Kevin Fontaine
Xavier Sarda

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This abstract presents a report on the proposed work programme, focus on cumulative risk assessment (CRA) for chemical risks, specifically pesticide residues in food. While not a scientific publication, this technical report aims to provide insights without including the fellow's data to avoid publication restrictions. This report focuses on addressing the question concerning the trigger value to perform a prospective CRA in case of a new maximum residue level (MRL) setting. The 1,000 margin of exposure (MOE) threshold value was tested and compared to preliminary ANSES results. Alternative thresholds were calculated and explored. The EU-FORA fellow selected two cumulative assessment groups (CAGs) related to acute craniofacial alterations and chronic thyroid effects. The fellow performed exposure assessments, integrating effects data, French monitoring data, processing factors, agricultural uses, MRLs and extrapolations into Monte Carlo risk assessment (MCRA). Retrospective cumulative exposures using MCRA were conducted for children, adults and a vulnerable group of childbearing women based on the French survey INCA3, identifying background levels at P99.9. The fellow also performed prospective assessments with MCRA, analysing results at P99.9 to evaluate the adequacy of the 1,000 MOE threshold. Alternative thresholds are discussed and proposed.

Indexed as

cumulative assessment groups (CAGs)cumulative risk assessmentEU Commission working group trigger valueMonte Carlo risk assessment (MCRA)MRL settingpesticide residuesretrospective and prospective cumulative risk assessment

Identifiers

PMID38047125
PMCPMC10687768

What OpenQuestion holds

Textmetadata
LicenceCC BY-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.